How to Design a Payment Routing Layer over L2 Networks

Layer 2 (L2) networks like Arbitrum, Optimism, and zkSync scale Ethereum by processing transactions off-chain. However, this creates a fragmented ecosystem where liquidity and users are siloed. A payment routing layer solves this by acting as a decentralized pathfinder, automatically selecting the best route for a value transfer between any two points in the L2 landscape. Its core function is to evaluate multiple variables—including bridge fees, network congestion, and finality times—to execute the most efficient cross-chain payment.

Designing this system requires a modular architecture. Key components include a liquidity graph to map asset availability across chains, a pricing oracle to fetch real-time gas and bridge costs, and a pathfinding algorithm (like a modified Dijkstra's) to compute the cheapest or fastest route. For developers, this often means building a set of smart contracts for locking/unlocking funds on each chain and a decentralized sequencer or keeper network to submit the batched transaction proofs.

Security is the paramount concern. A naive router that trusts a single bridge introduces a central point of failure. Robust designs use atomic transactions via protocols like Chainlink CCIP or LayerZero to ensure the payment either completes fully across all hops or fails entirely, preventing fund loss. Furthermore, the router should be permissionless and upgradeable via governance, allowing new L2s and bridges to be integrated without central oversight.

Consider a user on Arbitrum wanting to pay for a service on Base. A simple route might be: Arbitrum -> Ethereum Mainnet (via canonical bridge) -> Base (via Optimism's bridge). A more complex, cheaper route could be: Arbitrum -> Polygon zkEVM (via a third-party bridge) -> Base (via a liquidity network). The router's algorithm continuously evaluates these options. Implementing this requires querying APIs from services like SocketDL, Li.Fi, and the block explorers of each chain to build a cost matrix.

The end goal is a seamless user experience abstracting away blockchain complexity. Instead of manually bridging assets and paying multiple gas fees, a user approves a single transaction. The routing layer handles the rest, ultimately settling the payment on the destination chain. This infrastructure is critical for enabling scalable, chain-agnostic applications in DeFi, gaming, and global commerce, making multi-chain interactions feel as simple as a single-network transaction.

A payment routing layer is a system that finds and executes the optimal path for transferring value across multiple Layer 2 (L2) networks and their underlying Layer 1 (L1). To design one, you must first understand the core components: the L1 as the settlement and security layer (e.g., Ethereum), the various L2 architectures (e.g., Optimistic Rollups like Optimism and Arbitrum, ZK-Rollups like zkSync and Starknet, and Validiums), and the bridges or canonical messaging protocols that connect them. Each L2 has unique properties for finality, cost, and security that directly impact routing decisions.

You will need proficiency in smart contract development using Solidity or Vyper, as the core routing logic and fund custody will be implemented on-chain. Familiarity with cross-chain messaging standards like the Arbitrary Message Bridge (AMB) used by Optimism and Arbitrum, or the LayerZero and Wormhole protocols, is essential for instructing asset movements. Understanding token standards is also critical, particularly the difference between native L2 assets and bridged representations (e.g., USDC.e vs. native USDC), as these affect liquidity and composability.

A robust routing system requires a sophisticated off-chain component to discover paths and calculate costs. This involves building or integrating a graph-based data model where nodes represent networks and edges represent available bridges with associated fees and latency. You'll need skills in backend development (Node.js, Python, Go) to create services that continuously index network states—monitoring gas prices, bridge liquidity pools, and transaction finality times—to provide real-time routing recommendations.

Finally, security is paramount. You must design for the weakest link security model, where the safety of a cross-chain transaction depends on the least secure bridge in its path. This requires a deep understanding of bridge risk profiles (validated vs. external verification), economic security, and liquidity risks. Implementing features like atomic transactions (via Hash Time-Locked Contracts or HTLCs) and slippage protection are non-negotiable for a production-ready system. Always audit your contracts and consider formal verification for critical components.

The core of any effective payment routing system is its data layer. To route a transaction optimally across multiple Layer 2 (L2) networks like Arbitrum, Optimism, and Base, you need real-time intelligence on several key metrics. These include the current gas price (in Gwei) on each network, the estimated transaction confirmation time, and the bridge latency and cost for moving funds between chains. Without this aggregated data, routing decisions are based on stale or incomplete information, leading to suboptimal user experiences with high fees or slow transfers.

Data aggregation typically involves querying a combination of RPC endpoints, block explorer APIs, and specialized oracle services. For gas prices, you can call the eth_gasPrice method via RPC or use a service like Etherscan's Gas Tracker API. For bridge data, you need to integrate with bridge provider APIs (e.g., Hop, Across) to fetch current transfer times and fees. A robust aggregator will poll these sources every 15-30 seconds, normalizing the data into a consistent schema (e.g., {chainId, gasPriceWei, estimatedTimeMs, bridgeCostUsd}) for internal processing.

Implementing this requires a resilient backend service. Here's a simplified Node.js example using ethers.js to fetch gas data from multiple RPC providers concurrently, handling potential failures:

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async function getNetworkGasData(providers) {
  const promises = providers.map(async (provider) => {
    try {
      const feeData = await provider.getFeeData();
      return {
        gasPrice: feeData.gasPrice,
        lastBlock: await provider.getBlockNumber()
      };
    } catch (error) {
      console.error(`Failed to fetch from ${provider.network.name}`);
      return null;
    }
  });
  return Promise.all(promises);
}

This pattern ensures your system has fallbacks if one data source is unreachable.

Beyond basic metrics, advanced routing layers incorporate mempool data to predict future congestion and MEV (Maximal Extractable Value) activity to avoid front-running risks. Services like Blocknative's Mempool API or Flashbots Protect can provide this intelligence. By aggregating this broader dataset, your router can make more sophisticated decisions, such as delaying a low-priority payment on a congested network or selecting a bridge known for lower MEV risk, ultimately saving users money and improving reliability.

The final output of this aggregation step is a normalized, real-time network state object. This object serves as the single source of truth for the routing algorithm in Step 2. It must be updated frequently, validated for outliers (e.g., a spike due to an NFT mint), and stored in a low-latency database like Redis to enable fast queries. The quality of your routing decisions is directly proportional to the accuracy, freshness, and comprehensiveness of this aggregated data layer.

A payment routing algorithm for Layer 2 networks must solve a graph search problem. Each node in the graph represents a payment channel (e.g., on Arbitrum, Optimism, or a state channel), and each edge represents a possible hop with associated costs: a liquidity fee, a network gas fee, and a latency estimate for the hop's finality. The goal is to find the cheapest or fastest path from a source to a destination address that meets the payment amount, respecting each channel's available liquidity. This is more complex than a simple shortest-path search, as channel states (balances) are dynamic.

The most common approach is a modified Dijkstra's algorithm or A search*. You start from the payer's node and explore the graph. For each neighboring channel, you check if its balance can facilitate the payment amount. If it can, you calculate the cumulative cost (fees + estimated gas) and cumulative latency. You then push this potential path onto a priority queue, typically ordered by lowest cost or shortest estimated time. The algorithm continues until it finds a path to the payee or exhausts all possibilities. Libraries like js-graph-algorithms can provide the foundational graph structures.

Here is a simplified code skeleton for a cost-priority pathfinder using a priority queue:

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class Pathfinder {
  findCheapestPath(graph, source, dest, amount) {
    const costs = new Map();
    const prev = new Map();
    const queue = new PriorityQueue((a, b) => a.cost < b.cost);

    costs.set(source, 0);
    queue.push({ node: source, cost: 0 });

    while (!queue.isEmpty()) {
      const { node: current } = queue.pop();
      if (current === dest) break;

      for (const edge of graph.getEdges(current)) {
        if (edge.balance < amount) continue; // Skip illiquid channels
        const newCost = costs.get(current) + edge.fee + edge.estimatedGas;
        if (!costs.has(edge.to) || newCost < costs.get(edge.to)) {
          costs.set(edge.to, newCost);
          prev.set(edge.to, { from: current, via: edge });
          queue.push({ node: edge.to, cost: newCost });
        }
      }
    }
    // Reconstruct path from `prev` map...
  }
}

In production, you must integrate with real-time data providers. The graph isn't static; channel liquidity updates with every transaction. Services like Connext's Amarok or SocketDL offer APIs to query live liquidity and fee estimates across multiple L2s. Your algorithm should cache this data but include a TTL (Time-To-Live) and a fallback to refresh if a route fails during execution. Furthermore, for cross-L2 hops (e.g., Arbitrum → Polygon), you must account for the cost and delay of the canonical bridge or a third-party bridge like Hop or Across, modeling them as edges with higher latency and bridge-specific fees.

Finally, implement route ranking and fallback logic. The first found path may fail due to a race condition. A robust router generates multiple candidate paths (e.g., top 3 by cost). It attempts the best one, and if the transaction reverts (e.g., insufficient liquidity upon settlement), it immediately retries with the next candidate. This requires monitoring transaction status and having a fast failover mechanism. The entire pathfinding service should be stateless and scalable, often deployed as a separate microservice that your main payment layer queries via an API endpoint like GET /api/route?from=0x...&to=0x...&amount=1000000.

A transaction orchestrator is the decision engine of a cross-L2 payment system. Its primary function is to evaluate multiple possible routes for a fund transfer—considering factors like gas fees, bridge latency, and liquidity availability—and execute the optimal path. Instead of hardcoding a single bridge, the orchestrator uses a routing algorithm to compare live data from various sources, including on-chain oracles for gas prices and bridge APIs for status and fees. This dynamic approach is essential for minimizing cost and maximizing reliability in a multi-chain environment where conditions change by the minute.

The core logic involves several key components. First, a quote aggregator fetches potential transfer routes from integrated bridges (e.g., Hop, Across, Socket). For each route, it retrieves an estimated total cost, which includes source chain gas, bridge fees, and destination chain gas. Second, a constraint checker validates each route against user-defined parameters, such as maximum slippage, a deadline for completion, and a minimum received amount. Finally, a route scorer applies a weighted algorithm to rank the valid options, typically prioritizing the lowest total cost or fastest estimated time.

Here is a simplified TypeScript example of a route scoring function. This function takes an array of candidate routes, calculates a score for each, and returns the best option. The score is a weighted sum of normalized cost and estimated time.

typescriptCopy
interface RouteQuote {
  bridge: string;
  totalCostUSD: number;
  estimatedTimeMinutes: number;
  outputAmount: string;
}

function selectOptimalRoute(
  quotes: RouteQuote[], 
  costWeight: number = 0.7, 
  timeWeight: number = 0.3
): RouteQuote | null {
  if (quotes.length === 0) return null;

  const maxCost = Math.max(...quotes.map(q => q.totalCostUSD));
  const maxTime = Math.max(...quotes.map(q => q.estimatedTimeMinutes));

  const scoredQuotes = quotes.map(quote => {
    // Normalize values (lower is better)
    const normCost = 1 - (quote.totalCostUSD / maxCost);
    const normTime = 1 - (quote.estimatedTimeMinutes / maxTime);
    // Calculate weighted score
    const score = (normCost * costWeight) + (normTime * timeWeight);
    return { ...quote, score };
  });

  // Return the quote with the highest score
  return scoredQuotes.reduce((best, current) => 
    current.score > best.score ? current : best
  );
}

After selecting the optimal route, the orchestrator must construct and submit the transaction. This involves interacting with the chosen bridge's smart contract. The process typically follows a pattern: approve the bridge to spend the user's tokens on the source chain, then call the bridge's deposit or transfer function with the calculated parameters. For a robust system, the orchestrator should implement error handling and fallback logic—if a transaction fails or gets stuck, it can cancel and reroute using the next best option from its earlier analysis.

To operate reliably, the orchestrator needs access to real-time data. Integrate with services like Chainlink Gas Station for EIP-1559 fee estimation, DefiLlama's Bridge API for aggregated quotes, and Tenderly or OpenZeppelin Defender for transaction simulation and monitoring. Storing historical routing decisions and their outcomes (success/failure, actual cost vs. estimate) in a database allows for continuous improvement of the routing algorithm through periodic analysis and parameter tuning.

Finally, consider the user experience. The orchestrator can expose its functionality via a simple REST or GraphQL API, allowing frontends or other services to request a quote and later execute a transfer. For developers, providing an SDK that abstracts the complexity of gas estimation, nonce management, and receipt polling significantly reduces integration time. The end goal is a system where users specify what they want to send and where, and the orchestrator handles the complex how of cross-chain execution.

Transaction failures in a multi-L2 environment are inevitable due to network congestion, insufficient liquidity, or temporary chain reorganizations. Your routing logic must detect these failures programmatically. For on-chain payments, monitor for revert reasons like "insufficient funds" or "execution reverted". For off-chain or meta-transaction flows, implement timeouts and status polling. A critical first step is to categorize failures as retryable (e.g., low gas, temporary slippage) or terminal (e.g., invalid recipient, unsupported asset).

For retryable failures, design an intelligent retry mechanism. Instead of simple fixed delays, use an exponential backoff strategy. Consider adjusting parameters like gas price for Ethereum L1 settlements or submitting the transaction to a different sequencer for the same L2. Implement a circuit breaker pattern to stop retries after a defined threshold, preventing infinite loops and wasted fees. Log all retry attempts with context (chain ID, route attempted, error code) for later analysis and system improvement.

When a primary route fails, the system should have pre-defined fallback paths. This requires maintaining a ranked list of alternative bridges and liquidity pools for the same asset pair. For example, if a payment via Optimism's canonical bridge fails due to a proof verification delay, the fallback could route through a third-party bridge like Hop Protocol or Across. The logic should evaluate fallbacks based on real-time data: current latency, fees, and success rates, not just static configuration.

Incorporate atomicity and idempotency into your design to prevent double-spends or partial completion. For complex cross-chain payments, use a state machine to track the transaction's progress. If a step fails, the state machine should trigger a compensating action, like refunding the user on the source chain if the destination mint fails. Services like Chainlink CCIP or Axelar provide built-in execution guarantees that can simplify this, but you must still handle their potential failure modes.

Finally, establish clear user communication and refund protocols. For terminal failures, provide immediate, actionable error messages and initiate an automatic refund to the user's original wallet if possible. For retryable failures that eventually succeed, notify the user of the delay. Implement a dashboard or set of monitoring alerts (e.g., using Tenderly or custom indexers) to track failure rates per route, enabling proactive maintenance and de-prioritization of unreliable bridges or liquidity sources.

The core security model for a payment routing layer is defined by its on-chain settlement contract. This smart contract must be non-custodial, ensuring users retain control of their funds until a payment is finalized. It acts as the ultimate arbiter, adjudicating disputes and slashing malicious actors based on cryptographic proofs. For L2-specific routing, the contract must also verify proofs from each rollup's bridge or canonical messaging system, such as Optimism's L2OutputOracle or Arbitrum's Outbox. A critical vulnerability is the liveness assumption; if the sequencer for a destination L2 censors the final settlement transaction, the payment cannot be completed on-chain.

Economic security is enforced through bonding and slashing. Routers (or relayers) must stake a bond to participate. If a router commits fraud—for example, by signing two conflicting payment states—their bond is slashed. The bond size must be calibrated to exceed the potential profit from an attack, making fraud economically irrational. This creates a cryptoeconomic game similar to optimistic rollups but with shorter challenge periods optimized for payment speed. The system's security budget is the sum of all bonded capital, which must be large enough to deter coordinated attacks on high-value payment corridors.

Fee markets and incentives are essential for network liveness. Routers earn fees for providing liquidity and forwarding payments. The protocol should implement a dynamic fee algorithm that increases rewards during network congestion or low liquidity to attract more routers. However, fees must remain low enough to be competitive with traditional payment rails. A common model is a reverse auction where users submit bids, and routers compete to fulfill the payment at the lowest cost, with the protocol taking a small cut for the security pool. This aligns economic incentives between users seeking cheap payments and routers seeking profit.

Liquidity management presents a significant challenge. Routers must maintain balanced capital across multiple L2s and the base layer to avoid being unable to fulfill requests on a specific chain. Automated rebalancing strategies using cross-chain bridges or centralized exchanges are necessary but introduce cost and delay. Some designs use a hub-and-spoke model where a liquidity pool on a central chain (like Ethereum Mainnet) acts as a reserve, but this increases latency. The economic cost of idle capital and bridge fees must be factored into the router's pricing model to ensure long-term sustainability.

Finally, the system must account for L2 reorg risks and withdrawal delays. While L2s provide faster confirmation, their state can be reorganized before finalization on L1. A payment considered final on an L2 could be reverted, leading to a double-spend. The routing protocol must define finality based on L1 confirmation of the L2 state root, not just L2 block confirmations. This introduces a delay but is necessary for security. Users can opt for faster, less secure payments or slower, cryptographically guaranteed settlements, creating a security-latency tradeoff that the protocol must transparently expose.

You now have the foundational knowledge to design a payment routing layer. The key is to start with a clear use case and user flow. For example, are you building for a DApp that needs to pay gas fees on multiple chains, or a merchant processor aggregating payments from Optimism and Arbitrum? Your specific requirements will dictate whether you need a centralized sequencer for speed, a decentralized network of routers for censorship resistance, or a hybrid model. Define your latency, cost, and security tolerances first.

For implementation, begin with a proof-of-concept on a testnet. Use the Chainlink CCIP or Axelar GMP for generalized message passing to avoid building cross-chain infrastructure from scratch. Implement a simple router smart contract on two L2s, like Arbitrum Sepolia and Optimism Sepolia. Your contract should include functions to: lockFunds(address user, uint256 amount), emitCrossChainRequest(uint64 destinationChainSelector, bytes payload), and releaseFunds(address user, uint256 amount). Use a keeper or off-chain service to listen for events and trigger the cross-chain transaction.

Testing is critical. You must simulate and measure: latency from initiation to finality, cost breakdown per transaction (source gas, bridge fees, destination gas), and failure scenarios like destination chain congestion. Tools like Tenderly for simulation and Gelato for automated relayers can streamline this. Always implement a robust error handling and refund mechanism in your smart contracts to protect user funds if a route fails.

Looking ahead, consider integrating with account abstraction (ERC-4337) to enable gasless transactions sponsored by the routing layer, or using intent-based architectures where users specify a desired outcome (e.g., 'pay 100 USDC on Base') and the network finds the optimal route. The ecosystem is moving towards shared sequencing layers like Espresso and Astria, which could eventually provide native, atomic cross-rollup transaction bundles, simplifying router design.

To continue your learning, explore the documentation for cross-chain protocols (Wormhole, LayerZero), L2 client SDKs (viem, ethers), and monitoring tools (Blocknative, SocketDL). The design space is evolving rapidly, so a successful payment router is one that is modular, audited, and adaptable to new L2s and bridging primitives as they emerge.